<i>Contemporary Accounting Research</i>: A Retrospective between 1984 and 2021 using Bibliometric Analysis*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT This study critically evaluates research published by Contemporary Accounting Research ( CAR ) between 1984 and 2021 using bibliometric analysis. We examine the following: (i) CAR 's publication quality and the factors associated with its citations and (ii) CAR 's scope regarding research diversity, methods, authors geographical dispersion, and collaborative networks. The methodology permits observation of finer collaboration details and research patterns not apparent by simply categorizing the data. We use tools such as performance analysis, coauthorship analysis, bibliographic coupling, and regression analysis. The bibliometric analysis shows improvement in CAR 's CiteScore and source‐normalized impact per paper over time, consistent with publishing high‐quality research. Our analysis reveals that authors' geographical affiliations, research subject areas, and research methods are not systematically associated with citations across our various subsamples. A notable exception is that research on audit topics generates more citations than studies examining financial accounting topics. Other factors significantly and positively associated with citations include article age, article length, number of authors, order of author names, and number of references. We also show that CAR has become more diverse regarding author affiliations, subject areas, and research methods than most leading accounting journals. Only Accounting, Organizations and Society emerges as more diverse, thereby serving as a benchmark for CAR in the future. CAR should consider focusing on high‐interest areas to boost citations and tightening its acceptance criteria.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.105 | 0.265 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it